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Abstract
Customer segmentation plays a pivotal role in the banking industry, allowing institutions to customize marketing strategies and offers for specific customer segments. This paper investigates the use of the K-Means clustering algorithm, a machine learning technique, to group customers based on crucial financial attributes including account balance, balance checking frequency, purchase patterns, cash advances, and purchase frequency. The objective is to form well-defined clusters that reveal distinct customer profiles. By harnessing these insights, banks can design targeted marketing campaigns and personalized offers, enhancing customer engagement, fostering loyalty, and ultimately driving profitability.
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